Earticle

초록

영어

Respiratory diseases are one of the major causes of death worldwide. Therefore, research on respiratory disease classification using respiratory data is considered an important task. Previous studies mainly focused on respiratory disease classification using 2D feature extraction methods such as spectrograms and MFCCs. However, these methods have drawbacks such as long classification time and decreased accuracy as the number of respiratory disease types increases. To address this issue, we propose a solution that combines data with different dimensions to improve the performance of respiratory disease classification. We utilize the gammatone based spectrogram feature extraction method along with raw 1D respiratory data. By combining these two approaches, we can achieve both fast classification speed from 1D time-series models and high classification accuracy from 2D feature extraction methods. Our proposed respiratory disease classification study consists of four stages: data preprocessing, combined data generation, construction of a respiratory disease classification model, and decision-making for respiratory disease diagnosis. We validate our approach using a TCN (Temporal Convolutional Network) model and achieve a high respiratory disease classification accuracy of 98.93%. Moreover, our proposed method significantly reduces the training time for classification by more than four times compared to previous methods, thus demonstrating its superiority.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
III. METHOD
A. Data preprocessing
B. Data combinations
C. Temporal Convolution Network
IV. EXPERIMENT RESULT
A. Respiratory disease classification
B. Comparison of data training time
V. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• YeJin Kim [ Department of Computer Engineering Gachon University ]
• SaeBom Lee [ Department of Computer Engineering Gachon University ]
• Chang Choi [ Department of Computer Engineering Gachon University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004